Clustering, application, methods u 1

Department of CT III-B.Sc-CT VI Semester: 2019-20
16ED – Data Mining
Course: Data Mining Sub Code: 6ED
Google Classroom: q7b4gv Programme: B.Sc-CT
Unit: I Hour : 4
Faculty: Ms. A.SATHIYA PRIYA
clustering
Unit I Basic Data Mining Tasks

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Department of Computer Technology III BSC CT SEM V Year:
2019- 20
UNIT I Basic Data Mining Tasks6ED – Data Mining
SNAP TALK
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Department of Computer Technology III BSC CT SEM V Year:
2019- 20
UNIT I Basic Data Mining Tasks6ED – Data Mining
ATTENDANCE
3

Unit-I
Basic Data Mining Tasks - Data Mining Versus
Knowledge Discovery in Databases - Data Mining Issues
- Data Mining Matrices - Social Implications of Data
Mining - Data Mining from Data Base Perspective.
4
Department of Computer Science III-B.Sc-CT VI Semester: 2019-20
Unit I Basic Data Mining Tasks6ED – Data Mining

CLUSTERING
• What is Clustering?
• Clustering is the process of making a group of
abstract objects into classes of similar objects.
• A cluster of data objects can be treated as one group.
• While doing cluster analysis, we first partition the set
of data into groups based on data similarity and then
assign the labels to the groups.
5

Cont.,
The main advantage of clustering over classification is
that, it is adaptable to changes and helps single out
useful features that distinguish different groups.
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APPLICATIONS
• Applications of Cluster Analysis
• Clustering analysis is broadly used in many
applications such as market research, pattern
recognition, data analysis, and image processing.
• Clustering can also help marketers discover distinct
groups in their customer base. And they can
characterize their customer groups based on the
purchasing patterns.
7

Cont.,
• In the field of biology, it can be used to derive plant
and animal taxonomies, categorize genes with similar
functionalities and gain insight into structures
inherent to populations.
• Clustering also helps in identification of areas of
similar land use in an earth observation database. It
also helps in the identification of groups of houses in
a city according to house type, value, and geographic
location.
8

Cont.,
• Clustering also helps in classifying documents on the
web for information discovery.
• Clustering is also used in outlier detection
applications such as detection of credit card fraud.
• As a data mining function, cluster analysis serves as a
tool to gain insight into the distribution of data to
observe characteristics of each cluster.
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Requirements of Clustering in Data Mining
• He following points throw light on why clustering is
required in data mining −
• Scalability − We need highly scalable clustering
algorithms to deal with large databases.
• Ability to deal with different kinds of attributes −
Algorithms should be capable to be applied on any
kind of data such as interval-based (numerical) data,
categorical, and binary data.
10

Cont.,
• Discovery of clusters with attribute shape − The
clustering algorithm should be capable of detecting
clusters of arbitrary shape. They should not be
bounded to only distance measures that tend to find
spherical cluster of small sizes.
• High dimensionality − The clustering algorithm
should not only be able to handle low-dimensional
data but also the high dimensional space.
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Cont.,
• Ability to deal with noisy data − Databases contain
noisy, missing or erroneous data. Some algorithms
are sensitive to such data and may lead to poor
quality clusters.
• Interpretability − The clustering results should be
interpretable, comprehensible, and usable.
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Methods
• Clustering Methods
• Clustering methods can be classified into the
following categories −
• Partitioning Method
• Hierarchical Method
• Density-based Method
• Grid-Based Method
• Model-Based Method
• Constraint-based MethodPerspective.
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MCQ’s
1. ______is the process of making a group of abstract
objects into classes of similar objects
2. A cluster of data objects can be treated as _______
3. In the field of ______, it can be used to derive plant
It have Ability to deal with _____data
4. The clustering result should be
interpretable,comprehensible and __________.
5. The clustering algorithm should be capable of
detecting clusters of _______shape.
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MCQ’s
1. cluster
2. One group
3. Biology
4. uasble
5. arbitrary.
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THANK U
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Department of Computer Technology III BSC CT SEM V year: 2019-
20
6ED – Data Mining UNIT I Basic Data Mining Tasks

Clustering, application, methods u 1

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